Graph Neural Networks (GNNs) have attracted great attention recently because of their flexibility and effectiveness in describing 3D structures formed by discrete particles.
Graph Convolutional Neural Networks (GCNNs) model atom distribution using an undirected graph, and they simulate interactions among the atoms by passing messages among vertices.
Although most models only handle inputs with fixed format, GCNNs are capable of modeling highly variable structures with varying number of nodes.
This flexibility makes GCNNs have wide applications in different research fields such as computer vision and recommend systems.
There have been several applications in chemistry with excellent performance on property prediction [1]}, [2]}, [3]}, [4]} .
However, currently these methods have some noticeable disadvantages.
In particularly, the graph description abandons most 3D structural information, except the relative distance between connected vertices.
In chemistry terminology, this means that only bond lengths and coordination numbers are taken into consideration.
This overly simplification naturally fails to model atomic interactions accurately.
It is reasonable to assume that adding 3D information will improve model’s capability of making accurate predictions.
